Aiming at the problems of inadequate feature extraction and low recognition accuracy in human motion recognition, a human motion recognition model based on improved Longterm Recurrent Convolutional Network was proposed. Firstly, a LRCN model composed of multi-layer convolutional neural network and gated circulation unit is constructed. On this basis, the internal and external cycle layers are constructed. The role of the internal cycle layer is to obtain the internal time characteristics and spatial characteristics of the selected time window, while the role of the external cycle layer is to obtain the feature correlation and time correlation between the state information represented by the subsequence data. The proposed model was verified on three public data sets with higher accuracy than the traditional LRCN model. Then, it was tested and verified on the self-built data sets, and the recognition accuracy was 99.7%. The experimental results show that the recognition accuracy of this model is higher than that of the original model, which verifies the feasibility of this model.